Detection and Classification of Network Intrusion Using ILACR
The frequent changes in network environments, managing and updating the rule-based system has become a very challenging task for the administrator. Usually, rule-based systems work to make sense of a huge amount of alerts generated by the Intrusion Detection Systems (IDSs) every minute. Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, the authors address these two issues of accuracy and efficiency using conditional random fields and Integrated Layered Approach [ILACR].